Genetic Polymorphisms in N-acetyltransferase-2 and Microsomal Epoxide Hydrolase, Cumulative Cigarette Smoking, and Lung Cancer

Study Population.

The study was approved by the Human Subjects Committees of Massachusetts General Hospital and the Harvard School of Public Health, Boston. Eligible cases (patients with histologically confirmed incident lung cancers) at Massachusetts General Hospital were recruited between December 1992 and December 2000. Controls were recruited first among the friends and nonblood related family members of the lung cancer cases with no specific matching characteristics. In a small minority of cases (<10%), such individuals were not available, and controls were recruited among friends and nonblood related family members of nonlung cancer patients admitted to the cardiothoracic wards. This selection of controls was chosen in an attempt to balance cases and controls with respect to race, age, and smoking variables while avoiding overmatching. Interviewer-administered questionnaires collected information on demographic and detailed smoking histories from each subject.

Cumulative Smoking Exposure.

The questionnaire asked detailed smoking questions, including the age of onset of regular smoking, the average number of cigarettes smoked during different time periods, duration of smoking, and details on periods of smoking cessation. Pack-years were calculated as the product of the number of years an individual smoked and the average number of cigarettes smoked per day (converted into a standard pack of 20 cigarettes). Total periods of smoking cessation for periods >6 months were excluded from the total number of years of smoking in all calculations. Nonsmokers were defined as individuals who smoked fewer than 100 cigarettes in their lifetime.

NAT2 and mEH Genotyping.

Peripheral blood samples were obtained from each case or control, and DNA was extracted using the Puregene DNA Isolation Kit (Gentra Systems, Minneapolis, MN). NAT2 polymorphisms were detected using previously reported PCR-RFLP methods (22 , 23) . In brief, the four most common NAT2 polymorphisms, ⁴⁸¹C→T, ⁵⁹⁰G→A, ⁸⁰³A→G, and ⁸⁵⁷G→A, were identified by different restriction fragment analysis on digestion of the 547-bp PCR product, using restriction enzymes KpnI (481), TaqI (590), DdeI (803), and BamHI (857; New England BioLabs, Beverly, MA). DNA-digested PCR products were separated by size on a 2.5% agarose gel (Sigma Chemical Co., St. Louis, MO).

The genotyping methods for mEH polymorphisms have been described in detail (21) . In brief, two separate PCR assays were used to detect the polymorphisms in exons 3 and 4 of mEH, and EcoRV and RsaI enzyme digestion (New England BioLabs) were used respectively for restriction fragment analyses.

For quality control, a random 5% of the samples were repeated to assess the reproducibility of results, and a second investigator rechecked all data entry and analyses.

Statistical Analysis.

NAT2 and mEH genotypes were categorized according to their corresponding genetic polymorphisms. For NAT2, the wild type of each genetic polymorphism corresponds with rapid acetylation, and the variant genotypes correspond to slow acetylation. Thus, individuals carrying wild-type alleles (i.e., two “rapid” alleles, NAT2 ∗4/∗4) were designated as carrying the homozygous rapid acetylator genotype; individuals with only one copy of a rapid allele (heterozygous variant genotype, NAT2 ∗4) were identified as heterozygous rapid acetylators, and those with two “slow” alleles (homozygous variant genotypes; NAT2 ∗5A, ∗5B, ∗5C, ∗6, or ∗7) were labeled slow acetylators. A similar system was used to categorize the mEH genotype into very low, low, intermediate, and high activity groups, according to different mEH genotypes in exons 3 and 4 (21) .

Gene-environment interaction and gene-gene joint effects analyses involving multiple strata require enormous sample sizes. Combining different genotype strata on the basis of biological or epidemiological evidence has been a reasonable and practical approach. We combined NAT2 homozygous and heterozygous rapid acetylators into one group, comparing it with the slow acetylators, similar to approaches taken by others (9 , 11 , 12 , 14 , 23) . On the basis of our previous epidemiological research on mEH genotypes and lung cancer risk (21) , mEH was dichotomized into the genotype representing very low enzyme activity (homozygous for histidine in both exons and 4) versus all other genotypes combined.

Although individuals of all races were recruited for this study, we restricted our analyses to Caucasians to minimize the possibility of a different gene effect, i.e., effect modification by race or unmeasured confounders that vary by race. We included all Caucasians with complete information on age, gender, smoking status (non, ex-, and current smokers), pack-years of smoking, and years since smoking cessation (for ex-smokers). Logistic regression and GAM (24) were used to examine the relationship between the log odds of lung cancer and each covariate, after adjusting for possible confounding factors, such as age (years), gender, pack-years of smoking, smoking status, and years since smoking cessation (defined for ex-smokers; this variable was defined as zero for nonsmokers and current smokers). Some variables were transformed to approach normality. GAM allows the relationship between lung cancer risk and each covariate to be an unspecified smooth function. GAM plots showed a linear association between lung cancer risk and SR-PY but not for the untransformed variable. Subsequent analyses were based on logistic regression models, using SR-PY, age, and years since smoking cessation as continuous variables. In the NAT2 genotype-smoking primary interaction analysis, we included in our initial model all possible interactions among NAT2 genotype and two smoking variables (smoking status and SR-PY). To examine the combined effects of the NAT2 and mEH genotypes with smoking, we constructed a model that included all of the possible interactions among NAT2 and mEH genotypes and SR-PY, as well as any interactions between smoking status and either NAT2, mEH, or SR-PY that were included in the final models of NAT2 (or mEH) genotype-smoking interaction analysis. When the highest order interaction term was not significant, we considered models with only lower order interactions. Interaction terms were removed from the model when the likelihood ratio tests (comparing a candidate smaller model with all larger models in which it was nested) were not significant. Where appropriate, OR and 95% CI for the risk of lung cancer were calculated from these models. A lack of fit test was performed to summarize the goodness of fit of each logistic regression model (25) . Statistical analyses were all undertaken using the S-plus (MathSoft, Inc, Cambridge, MA) and SAS statistical packages (SAS Institute, Cary, NC).

Baseline Characteristics.

Over 85% of eligible cases and 90% of eligible controls participated in this study with no significant demographic differences (age and gender) between enrolled and unenrolled eligible participants. A total of 2592 (98.9%) of enrolled subjects were successfully genotyped for all NAT2 polymorphisms. The distributions of race, gender, age, and smoking characteristics for those with genotype data were similar to the corresponding distributions observed for the entire study population. Complete information on age, gender, and smoking variables was available for 2453 subjects (94.6%). We restricted our analysis to the 2365 Caucasians with complete data. Of these, there were 1115 lung cancer cases and 1250 controls. There was 100% concordance for the 5% randomly repeated samples and 100% concordance between the laboratory data and data entry. Histological and clinical stage data were described in detail in other reports (21 , 26) .

The mean age (and SD) of all cases was 65 ± 10.8 years (range 26–91) and of controls, 58.5 ± 12.3 years (range 19–100). Females represented 46.5% of cases and 54.2% of controls. Among cases, the mean pack-years of ex-smokers was 55.2 ± 35.9 and of current smokers, 64.3 ± 35.7; among controls, the numbers were 29.3 ± 27.7 and 38.1 ± 25.2, respectively. The years since smoking cessation for ex-smokers were 14.1 ± 11 and 19.3 ± 12 for cases and controls, respectively.

Of the seven widely studied genetic NAT2 polymorphisms, we identified the most frequently occurring four: ⁴⁸¹C→T, ⁵⁹⁰G→A, ⁸⁰³A→G, and ⁸⁵⁷G→A (11 , 22 , 23) . We also determined two other polymorphisms (³⁴¹T→C and ²⁸²C→T) in 312 random DNA samples, using standard methods (27) , and found that 99.7% of the ³⁴¹T→C polymorphism was linked with the ⁴⁸¹C→T or ⁸⁰³A→G polymorphisms, and 100% of the ²⁸²C→T polymorphism results were concordant with the ⁵⁹⁰G→A or ⁸⁵⁷G→A polymorphisms (2 , 7 , 13 , 14) . The seventh polymorphism (¹⁹¹G→A), generally restricted to African populations, was not evaluated in our Caucasian-restricted samples (7 , 13) .

Distribution of NAT2 and mEH Polymorphisms Among Cases and Controls.

All four polymorphic alleles of NAT2 were consistent with the Hardy-Weinberg equilibrium in our control population (P > 0.05, χ² goodness of fit). The frequencies of genotypes representing slow, heterozygous rapid, and homozygous rapid acetylators were 60.2, 35.8, and 4% in cases; these frequencies were very similar in controls (59.9, 35.8, and 4.2%, respectively; P > 0.05). As pack-years increased, the frequencies of slow acetylator genotypes decreased in cases (χ² trend test, P < 0.05) but not in controls (P > 0.05), e.g., the frequencies were 67% in cases and 56% in controls for nonsmokers and 61% in cases and 55% in controls with pack-years between 60 and 79, respectively.

A total of 896 cases and 1131 controls were genotyped for both NAT2 and mEH polymorphisms. The frequencies of the genotype combinations of NAT2 and mEH by pack-years and smoking status are shown in Table 1⇓ . Baseline characteristics of mEH genotypes have been reported previously (21) .

Association Between NAT2 Genotypes and Lung Cancer Risk.

No overall relationship between NAT2 genotypes and lung cancer risk was found. Compared with the slow acetylator genotypes, the crude ORs of lung cancer were 0.99 (95% CI, 0.84–1.18) for the heterozygous rapid acetylator genotype and 0.95 (95% CI, 0.63–1.43) for the homozygous rapid acetylator genotype. When adjusted for age, gender, SR-PY, smoking status, and years since smoking cessation, the ORs were 0.98 (95% CI, 0.80–1.20) and 0.76 (95% CI, 0.46–1.23), respectively. When the heterozygous and homozygous rapid acetylator genotypes were combined, the adjusted OR of the rapid versus slow acetylator genotypes was 0.96 (95% CI, 0.79–1.16).

Interaction Between NAT2 Genotype and Cumulative Cigarette Smoking in Lung Cancer Risk.

NAT2 genotypes representing homozygous and heterozygous rapid acetylators were combined into one group for this analysis. In the gene-tobacco smoking interaction analysis, the three- and two-way interactions between smoking status and genotype were not significant, and these were removed from the model. The final model included interactions between NAT2 genotype and SR-PY (P = 0.04) and between smoking status and SR-PY (P = 0.03) and was adjusted for age, gender, NAT2 genotype, SR-PY, smoking status, and years since smoking cessation. In this model, the ORs of the rapid versus slow acetylator genotypes increased as SR-PY increased (Table 2)⇓ . At 0 pack-years (nonsmokers), the fitted OR was 0.66 (95% CI, 0.44–0.99), indicating that the rapid acetylator genotype was protective against lung cancer. In contrast, at 80 pack-years, the fitted OR increased to 1.22 (95% CI, 0.89–1.67). No statistically significant results were found when cases of different histological types or disease stages were compared separately to all controls (P > 0.05 for all comparisons).

NAT2-mEH Genotypes and Their Interactions with Cumulative Cigarette Smoking in Lung Cancer Risk.

We investigated the interaction between NAT2 and mEH (gene-gene interaction) in lung cancer risk, because NAT2 and mEH have similar dual pathways in metabolizing tobacco smoking chemicals, and similar interactions between these genetic polymorphisms and SR-PY had been observed (21) . In the initial model, we included the three-way interaction between NAT2, mEH, and SR-PY, all nested two-way interactions, and the main effects. In addition, the interaction between smoking status and SR-PY was included, whereas all other interactions with smoking status were not included in this model, based on previous analyses of our data (21) . The three-way interaction term was not statistically significant (P > 0.25), and it was then removed from the model. The final NAT2 and mEH tobacco smoking interaction model included the interaction terms between NAT2 genotype and SR-PY (P = 0.03), mEH genotype and SR-PY (P < 0.01), and smoking status and SR-PY (P = 0.07), in addition to age, gender, NAT2 genotype, mEH genotype, SR-PY, smoking status, and years since smoking cessation. Compared with the combination of the NAT2 slow acetylator and very low mEH activity genotype, the ORs of each of the other three genotype combinations increased as pack-years increased. The ORs of the extreme combination of the NAT2 rapid acetylator and higher mEH activity genotypes increased most significantly (Table 2⇓ ; Fig. 1⇓ ). For nonsmokers (0 pack-years), the fitted OR for this comparison was 0.30 (95% CI, 0.14–0.62); at 80 pack-years, the fitted OR was 2.19 (95% CI, 1.26–3.81).

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Fig. 1.

Lung cancer ORs for different genotype combinations of NAT2 and mEH. ^a, the initial model (gene-gene interaction) included variables of age, gender, SR-PY, smoking status, years since smoking cessation, mEH genotype, NAT2 genotype, interaction between smoking status and SR-PY, all two-way interactions between NAT2, mEH, and SR-PY, and the three-way interaction between NAT2, mEH, and SR-PY. This model provides algebraically equivalent ORs to the gene-gene joint-effect model. The ORs were the extreme genotype combination of NAT2 rapid acetylator, higher mEH activity genotype versus NAT2 slow acetylator, and very low mEH activity genotype combination. ^b, the final model (no gene-gene interaction) included variables of age, gender, SR-PY, smoking status, years since smoking cessation, mEH genotype, NAT2 genotype, interaction between smoking status and SR-PY, interactions between mEH genotype and SR-PY, and interactions between NAT2 genotype and SR-PY. The ORs were differently combined NAT2-mEH genotypes versus the NAT2 slow acetylator and very low mEH activity genotype combination.

Although no significant interaction between NAT2, mEH, and SR-PY was observed, we wanted to examine the sensitivity of our results to the presence or absence of the three-way interaction between NAT2, mEH, and SR-PY. We compared the fitted ORs from our final model with the ORs from the model that included this three-way interaction and all nested two-way interactions and main effects (initial model). When comparing the final (non-gene-gene interaction) with the initial model (gene-gene interaction, equivalent to the joint-effects model), the fitted ORs were essentially unchanged (Fig. 1)⇓ , whereas the CIs were slightly wider under the initial model (Table 2)⇓ .